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Solving Large Clustering Problems with Meta-Heuristic Search

Publikation: Konferencebidrag › Paper › Forskning › peer review

Standard

Solving Large Clustering Problems with Meta-Heuristic Search. / Turkensteen, Marcel; Andersen, Kim Allan; Bang-Jensen, Jørgen.

2009. Paper præsenteret ved MIC 2009 - VIII Metaheuristic International Conference, Hamburg, Tyskland.

Publikation: Konferencebidrag › Paper › Forskning › peer review

Harvard

Turkensteen, M, Andersen, KA & Bang-Jensen, J 2009, 'Solving Large Clustering Problems with Meta-Heuristic Search', Paper fremlagt ved MIC 2009 - VIII Metaheuristic International Conference, Hamburg, Tyskland, 13/07/2009 - 16/07/2009. <http://www.smartframe.de/mic09/Home.html>

APA

Turkensteen, M., Andersen, K. A., & Bang-Jensen, J. (2009). Solving Large Clustering Problems with Meta-Heuristic Search. Paper præsenteret ved MIC 2009 - VIII Metaheuristic International Conference, Hamburg, Tyskland. http://www.smartframe.de/mic09/Home.html

CBE

Turkensteen M, Andersen KA, Bang-Jensen J. 2009. Solving Large Clustering Problems with Meta-Heuristic Search. Paper præsenteret ved MIC 2009 - VIII Metaheuristic International Conference, Hamburg, Tyskland.

MLA

Turkensteen, Marcel, Kim Allan Andersen og Jørgen Bang-Jensen Solving Large Clustering Problems with Meta-Heuristic Search. MIC 2009 - VIII Metaheuristic International Conference, 13 jul. 2009, Hamburg, Tyskland, Paper, 2009.

Vancouver

Turkensteen M, Andersen KA, Bang-Jensen J. Solving Large Clustering Problems with Meta-Heuristic Search. 2009. Paper præsenteret ved MIC 2009 - VIII Metaheuristic International Conference, Hamburg, Tyskland.

Author

Turkensteen, Marcel ; Andersen, Kim Allan ; Bang-Jensen, Jørgen. / Solving Large Clustering Problems with Meta-Heuristic Search. Paper præsenteret ved MIC 2009 - VIII Metaheuristic International Conference, Hamburg, Tyskland.

Bibtex

@conference{e59339e0559611deb1c3000ea68e967b,
title = "Solving Large Clustering Problems with Meta-Heuristic Search",
abstract = "In Clustering Problems, groups of similar subjects are to be retrieved from data sets. In this paper, Clustering Problems with the frequently used Minimum Sum-of-Squares Criterion are solved using meta-heuristic search. Tabu search has proved to be a successful methodology for solving optimization problems, but applications to large clustering problems are rare. The simulated annealing heuristic has mainly been applied to relatively small instances. In this paper, we implement tabu search and simulated annealing approaches and compare them to the commonly used k-means approach. We find that the meta-heuristic search methods are able to return solutions of very high quality.",
author = "Marcel Turkensteen and Andersen, {Kim Allan} and J{\o}rgen Bang-Jensen",
year = "2009",
language = "English",
note = "null ; Conference date: 13-07-2009 Through 16-07-2009",

}

RIS

TY - CONF

T1 - Solving Large Clustering Problems with Meta-Heuristic Search

AU - Turkensteen, Marcel

AU - Andersen, Kim Allan

AU - Bang-Jensen, Jørgen

PY - 2009

Y1 - 2009

N2 - In Clustering Problems, groups of similar subjects are to be retrieved from data sets. In this paper, Clustering Problems with the frequently used Minimum Sum-of-Squares Criterion are solved using meta-heuristic search. Tabu search has proved to be a successful methodology for solving optimization problems, but applications to large clustering problems are rare. The simulated annealing heuristic has mainly been applied to relatively small instances. In this paper, we implement tabu search and simulated annealing approaches and compare them to the commonly used k-means approach. We find that the meta-heuristic search methods are able to return solutions of very high quality.

AB - In Clustering Problems, groups of similar subjects are to be retrieved from data sets. In this paper, Clustering Problems with the frequently used Minimum Sum-of-Squares Criterion are solved using meta-heuristic search. Tabu search has proved to be a successful methodology for solving optimization problems, but applications to large clustering problems are rare. The simulated annealing heuristic has mainly been applied to relatively small instances. In this paper, we implement tabu search and simulated annealing approaches and compare them to the commonly used k-means approach. We find that the meta-heuristic search methods are able to return solutions of very high quality.

M3 - Paper

Y2 - 13 July 2009 through 16 July 2009

ER -